Many companies, over the years, have made significant investments, from data warehouses to analytics programs and have pursued the promised benefits of big data and advanced analytics. Data-analytics investments significantly increased value-added or operating profits, the simple revenue impact for consumer companies was considerably lower. The time frame of the analysis is important, since broader performance improvements from large-scale investments in data-analytics talent often don’t appear right away. B2B companies appear to confirm the intuition of executives struggling to uncover simple performance correlations. Read more at: http://www.mckinsey.com/industries/high-tech/our-insights/big-data-getting-a-better-read-on-performance

The development of mobile, IoT, and the cloud has increased the need of analytics to solve challenges in the customer, product, operations, and marketing domains. The established companies need to restructure their business and technology to increase their sales. Organizations need to involve cross-functional teams to establish data governance. Analytics 1.0 was data warehousing and business intelligence; Analytics 2.0 was big data, Hadoop, and NoSQL. Now in the era of Analytics 3.0, when tools make decisions and measure the impact. For more read the article written by chandramohan Kannusamy (Technical Architect) : http://data-informed.com/analytics-3-0-and-data-driven-transformation/

According to PwC's Sixth Annual Digital IQ Survey, it was found that collecting, analyzing, and leveraging customer data is a concern for 72% of business organizations, just 44% have taken action to address it. Customer data comes from many sources, like websites, email, social media, mobile, customer service, data warehouses, and more. Collecting, analyzing and using these customer data is a challenge. Marketers should leverage data effectively. Data management and analytics enable marketers to use data effectively.  Businesses need to gain competitive advantage by taking strategic decisions with customer insights. Further, self-learning marketing automation technologies combined with CRM tools allow marketers to set up relevant promotions or information to customers. Read more at: http://digitalmarketingmagazine.co.uk/digital-marketing-data/the-right-path-to-big-data/1327

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Data analysts want data in their data warehouse. When transaction-optimized databases were used for warehousing analysts would replicate data from manufacture systems into the data warehouse at the cost of change throughput. Analytics data warehouses are usually loaded numerous times each day, but often clients want to revive data every hour, every minute. This post outlines best practices for using Amazon Redshift for micro-batch loading and is planned for data architects, data modelers, and DBAs. Any data model can be laden via micro-batching, but data prepared as a time series is often ideal for this type of loading. To make the COPY command as capable as probable, ask it to do as little as promising . Read more at:

http://blogs.aws.amazon.com/bigdata/post/Tx2ANLN1PGELDJU/Best-Practices-for-Micro-Batch-Loading-on-Amazon-Redshift

Teradata launched a technology called QueryGrid. It aims to extract analytical data from multiple systems storing, both big and little data. QueryGrid can bridge the division between data warehouses and big data. To know the model of QueryGrid, follow the article by Larry Dignan, Editor in Chief of ZDNet and SmartPlanet. - See more at:

http://www.zdnet.com/teradata-launches-querygrid-one-workflow-for-big-little-data-7000028044/